Using Reflectance Sensors in Agronomy and Weed Science¹

Abstract

Weed-detecting reflectance sensors were modified to allow selective interrogation of the near infrared–red ratio to estimate differences in plant biomass. Sampling was programmed to correspond to the forward movement of the field of view of the sensors. There was a linear relationship (r 2 > 0.80) between actual biomass and crop canopy analyzer (CCA) values up to 2,000 kg/ha for winter wheat sequentially thinned to create different amounts of biomass and up to 1,000 kg/ha for spring wheat sampled at different stages of development. At higher amounts of biomass the sensors underestimated the actual biomass. A linear relationship (r 2 = 0.73) was obtained with the CCA for the biomass of 76 chickpea cultivars at 500 growing degree days (GDD500). The reflectance sensors were used to determine differences in the herbicide response of soybean cultivars sprayed with increasing rates of herbicides. The CCA data resulted in better dose–response relationships than did biomass data for bromoxynil at 0.8 kg ai/ha and glyphosate at 1.35 kg ai/ha. There was no phytotoxicity to soybean with imazethapyr at 1.44 kg ai/ha. The method offers a quick and nondestructive means to measure differences in early-season crop growth. It also has potential in selecting crop cultivars with greater seedling vigor, as an indicator of crop nutrient status, in plant disease assessment, in determining crop cultivar responses to increasing herbicide dose rates, in weed mapping, and in studying temporal changes in crop or weed biomass.

Discriminating cropping systems and agro-environmental measures by remote sensing

“…Other techniques are based on the use of object segmentation algorithms and on the construction of a hierarchical network of image objects, and works on the treatment of several data types simultaneously such as pixel values, object features and hierarchical relationships (Baatz and Schäpe, 1999). Felton et al (2002), and Thorp and Tian (2004) reviewed the uses of remote sensing in agriculture, and reported that, to date, the majority of studies on discriminating plant species in cultivation systems have involved discrete broadband remote sensing using multispectral sensors. Spectral reflectance differences can be enhanced by using vegetation indices, which are mathematical (ratios or linear) combinations between bands or selected wavelengths.…”

Mapping Ridolfia segetum patches in sunflower crop using remote sensing

“…Remote sensing of weed canopies could be more efficient than field measurements, thus the interest in using this technology for developing weed distribution maps has increased in recent years. The importance of remote sensing in site‐specific agriculture has been widely reviewed by Lamb and Brown (2001), Felton et al. (2002), Radhakrishnan et al.…”

Spectral discrimination of Ridolfia segetum and sunflower as affected by phenological stage

“…Thus interest in using this technology for developing weed distribution maps has increased in recent years. The importance of remote sensing in site‐specific agriculture has been widely reviewed by Felton et al. (2002) and Radhakrishnan et al.…”